Method for classifying leaves utilizing venation features

Abstract

A method for classifying a plant leaf by utilizing the feature points of venation has been developed. A sample image of the venation can be extracted by utilizing a Curvature Scale Space (CSS) Corner Detection Algorithm. The sample image is treated to thicken the venation and increase the contrast through the retrieval unit prior to applying the Canny Edge Detection technology. The feature point, Branching Point and End Point is detected at each point where the calculated curvature angle is a local maximum. The distribution of the feature points of the extracted venation is calculated by applying a Parzen Window non-parametric estimation method.

1 . A method for classifying a plant leaf by utilizing a feature point of venation, the method comprising the steps of:
 extracting a sample image of venation from a leaf for inputting to an input unit ( 120 ), then searching for a similar sample image of a specimen venation to retrieve among the stored sample images of the various plant leaves through a retrieval unit ( 130 ) in a Data Supplying Computer ( 100 ) (S 401 ),   detecting a series of Branching Points (BP) and Ending Points (EP) from the extracted sample image of venation by applying a Curvature Scale Space (CSS) Corner Detection Algorithm (S 405 ),   classifying the extracted venation based on the detected BP and EP through an analysis unit ( 140 ) (S 407 ),   verifying whether the detected feature points, BP and EP are distributed along a line or around a point by calculating the probability density function (PDF) of the feature points, BP and EP of the extracted venation using a Parzen Window non-parametric estimation technique (S 409 ),   according to the PDFs calculated in the previous step, analyzing the distribution of feature points, BP and EP along a longitudinal line to detect a parallel venation if they are clustered around a point at the top and/or bottom, or a non-parallel venation, if the feature points, BP and EP are distributed along the longitudinal line (S 410 ),   based on the previous decision step, if it is a case of the parallel venation, analyzing the distribution of the BP along the longitudinal and lateral lines (S 420 ), further verifying whether the BP are densely clustered at the top while the BP form a line at the bottom (S 425 ), and   classifying a second parallel venation if the BP are distributed along a line at the bottom end(S 427 ), and further classifying a first parallel venation if the BP at the upper are densely clustered around a point, while the BP at the bottom end are densely clustered around a point (S 428 ).   
   
   
       2 . A method for classifying a plant leaf according to  claim 1 , wherein said analysis of the distribution of feature points further comprises the step of:
 if a parallel venation has not been detected according to the previous decision step, analyzing the distribution of the BP along the longitudinal and lateral lines (S 430 ), and investigating whether the BP are distributed along a longitudinal line running from the top to bottom of the leaf (S 435 ), and   classifying a pinnate venation if the BP are distributed along a line from the top to the bottom (S 436 ), and classifying a palmate venation if the BP are densely clustered around a point at the lower bottom (S 439 ).   
   
   
       3 . A method for classifying a plant leaf according to  claim 1 , wherein said method for extracting the sample image of venation applies a Canny Edge Detection technology to detect the shape of the feature points of the extracted venation. 
   
   
       4 . A method for classifying a plant leaf according to  claim 1 , wherein said sample image of the extracted venation is treated to thicken the venation and increase the contrast through the retrieval unit ( 130 ) prior to applying the Canny Edge Detection technology. 
   
   
       5 . A method for classifying a plant leaf according to  claim 3 , wherein said sample image of the extracted venation is treated to thicken the venation and increase the contrast through the retrieval unit ( 130 ) prior to applying the Canny Edge Detection technology. 
   
   
       6 . A method for classifying a plant leaf according to  claim 1 , wherein said BP of the sample image calculates a curvature angle at the maximum points of the extracted venation and selects the points less than 90 degrees.